Overview

Dataset statistics

Number of variables14
Number of observations7998
Missing cells14551
Missing cells (%)13.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory874.9 KiB
Average record size in memory112.0 B

Variable types

Categorical1
Numeric13

Variable descriptions

co_gtstuendlich gemittelte CO-Konzentration
pt08_s1_costuendlich gemittelte Sensorreaktion (nominell auf CO ausgerichtet) (Zinnoxid)
nmhc_gtstuendlich gemittelte Gesamtkonzentration an nicht-metanischem Kohlenwasserstoff
c6h6_gtstuendlich gemittelte Benzolkonzentration
pt08_s2_nmhcstuendlich gemittelte Sensorreaktion (nominell auf NMHC ausgerichtet) (Titandioxid)
nox_gtEchte stuendlich gemittelte NOx-Konzentration
pt08_s3_noxstuendlich gemitteltes Sensoransprechverhalten (nominell auf NOx ausgerichtet)
no2_gtstuendlich gemittelte NO2-Konzentration
pt08_s4_no2stuendlich gemittelte Sensorreaktion (nominell auf NO2 ausgerichtet) (Wolframoxid)
pt08_s5_o3stuendlich gemitteltes Sensoransprechverhalten (nominell O3-bezogen) (Indiumoxid)
tTemperatur
rhRelative Luftfeuchtigkeit
ahAbsolute Luftfeuchtigkeit
monthMonate der Erfassung
hourStunden der Erfassung

Alerts

date has a high cardinality: 7998 distinct values High cardinality
co_gt is highly correlated with pt08_s1_co and 8 other fieldsHigh correlation
pt08_s1_co is highly correlated with co_gt and 8 other fieldsHigh correlation
nmhc_gt is highly correlated with co_gt and 9 other fieldsHigh correlation
c6h6_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with co_gt and 8 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s3_nox is highly correlated with co_gt and 8 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with co_gt and 8 other fieldsHigh correlation
t is highly correlated with nmhc_gt and 3 other fieldsHigh correlation
rh is highly correlated with tHigh correlation
ah is highly correlated with pt08_s4_no2 and 1 other fieldsHigh correlation
co_gt is highly correlated with pt08_s1_co and 8 other fieldsHigh correlation
pt08_s1_co is highly correlated with co_gt and 8 other fieldsHigh correlation
nmhc_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
c6h6_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with co_gt and 8 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s3_nox is highly correlated with co_gt and 8 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with co_gt and 8 other fieldsHigh correlation
t is highly correlated with pt08_s4_no2 and 2 other fieldsHigh correlation
rh is highly correlated with tHigh correlation
ah is highly correlated with pt08_s4_no2 and 1 other fieldsHigh correlation
co_gt is highly correlated with pt08_s1_co and 7 other fieldsHigh correlation
pt08_s1_co is highly correlated with co_gt and 8 other fieldsHigh correlation
nmhc_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
c6h6_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with co_gt and 8 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s3_nox is highly correlated with co_gt and 6 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 6 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with pt08_s1_co and 3 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with co_gt and 7 other fieldsHigh correlation
co_gt is highly correlated with pt08_s1_co and 8 other fieldsHigh correlation
pt08_s1_co is highly correlated with co_gt and 8 other fieldsHigh correlation
nmhc_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
c6h6_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s2_nmhc is highly correlated with co_gt and 8 other fieldsHigh correlation
nox_gt is highly correlated with co_gt and 8 other fieldsHigh correlation
pt08_s3_nox is highly correlated with co_gt and 8 other fieldsHigh correlation
no2_gt is highly correlated with co_gt and 7 other fieldsHigh correlation
pt08_s4_no2 is highly correlated with co_gt and 9 other fieldsHigh correlation
pt08_s5_o3 is highly correlated with co_gt and 8 other fieldsHigh correlation
t is highly correlated with pt08_s4_no2 and 2 other fieldsHigh correlation
rh is highly correlated with tHigh correlation
ah is highly correlated with pt08_s4_no2 and 1 other fieldsHigh correlation
co_gt has 1654 (20.7%) missing values Missing
pt08_s1_co has 289 (3.6%) missing values Missing
nmhc_gt has 7084 (88.6%) missing values Missing
c6h6_gt has 289 (3.6%) missing values Missing
pt08_s2_nmhc has 289 (3.6%) missing values Missing
nox_gt has 1604 (20.1%) missing values Missing
pt08_s3_nox has 289 (3.6%) missing values Missing
no2_gt has 1607 (20.1%) missing values Missing
pt08_s4_no2 has 289 (3.6%) missing values Missing
pt08_s5_o3 has 289 (3.6%) missing values Missing
t has 289 (3.6%) missing values Missing
rh has 289 (3.6%) missing values Missing
ah has 290 (3.6%) missing values Missing
date is uniformly distributed Uniform
date has unique values Unique

Reproduction

Analysis started2022-05-02 20:41:19.391609
Analysis finished2022-05-02 20:41:44.766359
Duration25.37 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

date
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct7998
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size62.6 KiB
2004-03-10 18:00:00
 
1
2004-10-18 17:00:00
 
1
2004-10-19 06:00:00
 
1
2004-10-19 05:00:00
 
1
2004-10-19 04:00:00
 
1
Other values (7993)
7993 

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7998 ?
Unique (%)100.0%

Sample

1st row2004-03-10 18:00:00
2nd row2004-03-10 19:00:00
3rd row2004-03-10 20:00:00
4th row2004-03-10 21:00:00
5th row2004-03-10 22:00:00

Common Values

ValueCountFrequency (%)
2004-03-10 18:00:001
 
< 0.1%
2004-10-18 17:00:001
 
< 0.1%
2004-10-19 06:00:001
 
< 0.1%
2004-10-19 05:00:001
 
< 0.1%
2004-10-19 04:00:001
 
< 0.1%
2004-10-19 03:00:001
 
< 0.1%
2004-10-19 02:00:001
 
< 0.1%
2004-10-19 01:00:001
 
< 0.1%
2004-10-19 00:00:001
 
< 0.1%
2004-10-18 23:00:001
 
< 0.1%
Other values (7988)7988
99.9%

Length

2022-05-02T22:41:44.887973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
19:00:00334
 
2.1%
20:00:00334
 
2.1%
18:00:00334
 
2.1%
23:00:00334
 
2.1%
22:00:00334
 
2.1%
21:00:00334
 
2.1%
07:00:00333
 
2.1%
08:00:00333
 
2.1%
09:00:00333
 
2.1%
10:00:00333
 
2.1%
Other values (348)12660
79.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

co_gt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte CO-Konzentration

Distinct95
Distinct (%)1.5%
Missing1654
Missing (%)20.7%
Infinite0
Infinite (%)0.0%
Mean2.185734552
Minimum0.1
Maximum11.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:45.046830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.5
Q11.1
median1.9
Q32.9
95-th percentile5
Maximum11.9
Range11.8
Interquartile range (IQR)1.8

Descriptive statistics

Standard deviation1.476284701
Coefficient of variation (CV)0.6754181103
Kurtosis2.726542297
Mean2.185734552
Median Absolute Deviation (MAD)0.9
Skewness1.368798365
Sum13866.3
Variance2.179416519
MonotonicityNot monotonic
2022-05-02T22:41:45.210264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1240
 
3.0%
1.6227
 
2.8%
1.7221
 
2.8%
1.4215
 
2.7%
1.5215
 
2.7%
0.7214
 
2.7%
1.3212
 
2.7%
1.2209
 
2.6%
1.1203
 
2.5%
0.6197
 
2.5%
Other values (85)4191
52.4%
(Missing)1654
 
20.7%
ValueCountFrequency (%)
0.125
 
0.3%
0.237
 
0.5%
0.387
 
1.1%
0.4134
1.7%
0.5181
2.3%
0.6197
2.5%
0.7214
2.7%
0.8197
2.5%
0.9192
2.4%
1240
3.0%
ValueCountFrequency (%)
11.91
< 0.1%
11.51
< 0.1%
10.22
< 0.1%
10.11
< 0.1%
9.91
< 0.1%
9.51
< 0.1%
9.41
< 0.1%
9.31
< 0.1%
9.21
< 0.1%
9.12
< 0.1%

pt08_s1_co
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte Sensorreaktion (nominell auf CO ausgerichtet) (Zinnoxid)

Distinct1027
Distinct (%)13.3%
Missing289
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean1098.303412
Minimum647
Maximum2040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:45.361547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum647
5-th percentile806
Q1932
median1061
Q31234
95-th percentile1510
Maximum2040
Range1393
Interquartile range (IQR)302

Descriptive statistics

Standard deviation219.9384571
Coefficient of variation (CV)0.2002529126
Kurtosis0.3247265541
Mean1098.303412
Median Absolute Deviation (MAD)145
Skewness0.7563541242
Sum8466821
Variance48372.92492
MonotonicityNot monotonic
2022-05-02T22:41:45.509207image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96925
 
0.3%
97324
 
0.3%
110024
 
0.3%
92524
 
0.3%
89222
 
0.3%
96622
 
0.3%
96222
 
0.3%
105022
 
0.3%
105321
 
0.3%
98721
 
0.3%
Other values (1017)7482
93.5%
(Missing)289
 
3.6%
ValueCountFrequency (%)
6471
 
< 0.1%
6491
 
< 0.1%
6551
 
< 0.1%
6673
< 0.1%
6691
 
< 0.1%
6761
 
< 0.1%
6781
 
< 0.1%
6791
 
< 0.1%
6811
 
< 0.1%
6832
< 0.1%
ValueCountFrequency (%)
20401
< 0.1%
20081
< 0.1%
19821
< 0.1%
19751
< 0.1%
19731
< 0.1%
19611
< 0.1%
19561
< 0.1%
19341
< 0.1%
19181
< 0.1%
19171
< 0.1%

nmhc_gt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte Gesamtkonzentration an nicht-metanischem Kohlenwasserstoff

Distinct429
Distinct (%)46.9%
Missing7084
Missing (%)88.6%
Infinite0
Infinite (%)0.0%
Mean218.8118162
Minimum7
Maximum1189
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:45.659429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile28.65
Q167
median150
Q3297
95-th percentile661.4
Maximum1189
Range1182
Interquartile range (IQR)230

Descriptive statistics

Standard deviation204.4599213
Coefficient of variation (CV)0.9344098724
Kurtosis2.270289034
Mean218.8118162
Median Absolute Deviation (MAD)94
Skewness1.557017103
Sum199994
Variance41803.8594
MonotonicityNot monotonic
2022-05-02T22:41:45.804856image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6614
 
0.2%
409
 
0.1%
299
 
0.1%
888
 
0.1%
938
 
0.1%
847
 
0.1%
557
 
0.1%
957
 
0.1%
607
 
0.1%
577
 
0.1%
Other values (419)831
 
10.4%
(Missing)7084
88.6%
ValueCountFrequency (%)
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
101
 
< 0.1%
111
 
< 0.1%
142
< 0.1%
161
 
< 0.1%
174
0.1%
182
< 0.1%
192
< 0.1%
ValueCountFrequency (%)
11891
< 0.1%
11291
< 0.1%
10841
< 0.1%
10421
< 0.1%
9741
< 0.1%
9261
< 0.1%
8991
< 0.1%
8801
< 0.1%
8721
< 0.1%
8401
< 0.1%

c6h6_gt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte Benzolkonzentration

Distinct405
Distinct (%)5.3%
Missing289
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean10.4582047
Minimum0.1
Maximum63.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:45.949028image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile1.8
Q14.7
median8.6
Q314.4
95-th percentile25.06
Maximum63.7
Range63.6
Interquartile range (IQR)9.7

Descriptive statistics

Standard deviation7.580281907
Coefficient of variation (CV)0.724816747
Kurtosis2.421728425
Mean10.4582047
Median Absolute Deviation (MAD)4.5
Skewness1.336666649
Sum80622.3
Variance57.46067379
MonotonicityNot monotonic
2022-05-02T22:41:46.088166image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
368
 
0.9%
3.668
 
0.9%
2.867
 
0.8%
467
 
0.8%
3.865
 
0.8%
2.663
 
0.8%
5.462
 
0.8%
662
 
0.8%
3.161
 
0.8%
6.459
 
0.7%
Other values (395)7067
88.4%
(Missing)289
 
3.6%
ValueCountFrequency (%)
0.12
 
< 0.1%
0.25
 
0.1%
0.37
 
0.1%
0.413
0.2%
0.515
0.2%
0.617
0.2%
0.725
0.3%
0.818
0.2%
0.919
0.2%
117
0.2%
ValueCountFrequency (%)
63.71
< 0.1%
52.11
< 0.1%
50.81
< 0.1%
50.71
< 0.1%
50.61
< 0.1%
49.51
< 0.1%
49.41
< 0.1%
48.21
< 0.1%
47.71
< 0.1%
47.51
< 0.1%

pt08_s2_nmhc
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte Sensorreaktion (nominell auf NMHC ausgerichtet) (Titandioxid)

Distinct1221
Distinct (%)15.8%
Missing289
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean953.5794526
Minimum383
Maximum2214
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:46.234082image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum383
5-th percentile570.4
Q1749
median925
Q31130
95-th percentile1430.6
Maximum2214
Range1831
Interquartile range (IQR)381

Descriptive statistics

Standard deviation268.0359112
Coefficient of variation (CV)0.2810839836
Kurtosis0.05623062993
Mean953.5794526
Median Absolute Deviation (MAD)189
Skewness0.5409852901
Sum7351144
Variance71843.24969
MonotonicityNot monotonic
2022-05-02T22:41:46.372944image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
85322
 
0.3%
77620
 
0.3%
85920
 
0.3%
81419
 
0.2%
88019
 
0.2%
76919
 
0.2%
85018
 
0.2%
96218
 
0.2%
90018
 
0.2%
89018
 
0.2%
Other values (1211)7518
94.0%
(Missing)289
 
3.6%
ValueCountFrequency (%)
3832
< 0.1%
3881
 
< 0.1%
3901
 
< 0.1%
3971
 
< 0.1%
3991
 
< 0.1%
4021
 
< 0.1%
4071
 
< 0.1%
4101
 
< 0.1%
4123
< 0.1%
4151
 
< 0.1%
ValueCountFrequency (%)
22141
< 0.1%
20071
< 0.1%
19831
< 0.1%
19811
< 0.1%
19801
< 0.1%
19591
< 0.1%
19581
< 0.1%
19351
< 0.1%
19241
< 0.1%
19201
< 0.1%

nox_gt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Echte stuendlich gemittelte NOx-Konzentration

Distinct890
Distinct (%)13.9%
Missing1604
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean236.6709415
Minimum2
Maximum1479
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:46.515602image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile35
Q189
median165
Q3309
95-th percentile703.35
Maximum1479
Range1477
Interquartile range (IQR)220

Descriptive statistics

Standard deviation216.1546869
Coefficient of variation (CV)0.9133131662
Kurtosis3.840367645
Mean236.6709415
Median Absolute Deviation (MAD)94
Skewness1.839143028
Sum1513274
Variance46722.84869
MonotonicityNot monotonic
2022-05-02T22:41:46.651068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8939
 
0.5%
6537
 
0.5%
4136
 
0.5%
5732
 
0.4%
5132
 
0.4%
6131
 
0.4%
18031
 
0.4%
4631
 
0.4%
9331
 
0.4%
7230
 
0.4%
Other values (880)6064
75.8%
(Missing)1604
 
20.1%
ValueCountFrequency (%)
21
 
< 0.1%
41
 
< 0.1%
61
 
< 0.1%
71
 
< 0.1%
81
 
< 0.1%
91
 
< 0.1%
103
< 0.1%
114
0.1%
124
0.1%
134
0.1%
ValueCountFrequency (%)
14791
< 0.1%
13892
< 0.1%
13691
< 0.1%
13581
< 0.1%
13451
< 0.1%
13101
< 0.1%
13011
< 0.1%
12901
< 0.1%
12531
< 0.1%
12471
< 0.1%

pt08_s3_nox
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemitteltes Sensoransprechverhalten (nominell auf NOx ausgerichtet)

Distinct1203
Distinct (%)15.6%
Missing289
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean848.7626151
Minimum322
Maximum2683
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:46.787482image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum322
5-th percentile491
Q1672
median818
Q3984
95-th percentile1307.2
Maximum2683
Range2361
Interquartile range (IQR)312

Descriptive statistics

Standard deviation259.6389982
Coefficient of variation (CV)0.3059029622
Kurtosis2.740731223
Mean848.7626151
Median Absolute Deviation (MAD)155
Skewness1.117558096
Sum6543111
Variance67412.40939
MonotonicityNot monotonic
2022-05-02T22:41:47.015989image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73324
 
0.3%
84624
 
0.3%
76722
 
0.3%
81621
 
0.3%
80021
 
0.3%
87620
 
0.3%
68520
 
0.3%
76520
 
0.3%
74819
 
0.2%
76419
 
0.2%
Other values (1193)7499
93.8%
(Missing)289
 
3.6%
ValueCountFrequency (%)
3221
< 0.1%
3252
< 0.1%
3281
< 0.1%
3301
< 0.1%
3341
< 0.1%
3351
< 0.1%
3402
< 0.1%
3411
< 0.1%
3472
< 0.1%
3481
< 0.1%
ValueCountFrequency (%)
26831
< 0.1%
25591
< 0.1%
25421
< 0.1%
23311
< 0.1%
23271
< 0.1%
23181
< 0.1%
22941
< 0.1%
21211
< 0.1%
20952
< 0.1%
20811
< 0.1%

no2_gt
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte NO2-Konzentration

Distinct267
Distinct (%)4.2%
Missing1607
Missing (%)20.1%
Infinite0
Infinite (%)0.0%
Mean106.8000313
Minimum2
Maximum333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:47.147129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile41
Q173
median103
Q3133
95-th percentile187
Maximum333
Range331
Interquartile range (IQR)60

Descriptive statistics

Standard deviation44.9179855
Coefficient of variation (CV)0.4205802653
Kurtosis0.574688148
Mean106.8000313
Median Absolute Deviation (MAD)30
Skewness0.602514942
Sum682559
Variance2017.625421
MonotonicityNot monotonic
2022-05-02T22:41:47.278277image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9772
 
0.9%
9569
 
0.9%
10167
 
0.8%
9666
 
0.8%
11465
 
0.8%
12165
 
0.8%
10764
 
0.8%
11964
 
0.8%
11064
 
0.8%
11663
 
0.8%
Other values (257)5732
71.7%
(Missing)1607
 
20.1%
ValueCountFrequency (%)
21
 
< 0.1%
31
 
< 0.1%
52
 
< 0.1%
71
 
< 0.1%
82
 
< 0.1%
92
 
< 0.1%
112
 
< 0.1%
122
 
< 0.1%
131
 
< 0.1%
145
0.1%
ValueCountFrequency (%)
3331
< 0.1%
3221
< 0.1%
3101
< 0.1%
3091
< 0.1%
3061
< 0.1%
3011
< 0.1%
2881
< 0.1%
2851
< 0.1%
2832
< 0.1%
2822
< 0.1%

pt08_s4_no2
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemittelte Sensorreaktion (nominell auf NO2 ausgerichtet) (Wolframoxid)

Distinct1549
Distinct (%)20.1%
Missing289
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean1507.352834
Minimum657
Maximum2775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:47.414012image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum657
5-th percentile940
Q11305
median1508
Q31707
95-th percentile2059
Maximum2775
Range2118
Interquartile range (IQR)402

Descriptive statistics

Standard deviation331.314334
Coefficient of variation (CV)0.2197987932
Kurtosis0.3117142139
Mean1507.352834
Median Absolute Deviation (MAD)201
Skewness0.1840650459
Sum11620183
Variance109769.1879
MonotonicityNot monotonic
2022-05-02T22:41:47.534575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
158022
 
0.3%
153920
 
0.3%
163819
 
0.2%
148819
 
0.2%
146719
 
0.2%
141818
 
0.2%
157017
 
0.2%
151117
 
0.2%
160416
 
0.2%
147316
 
0.2%
Other values (1539)7526
94.1%
(Missing)289
 
3.6%
ValueCountFrequency (%)
6571
< 0.1%
6671
< 0.1%
6681
< 0.1%
6741
< 0.1%
6821
< 0.1%
6851
< 0.1%
6971
< 0.1%
6982
< 0.1%
7022
< 0.1%
7092
< 0.1%
ValueCountFrequency (%)
27751
< 0.1%
27461
< 0.1%
26911
< 0.1%
26841
< 0.1%
26791
< 0.1%
26671
< 0.1%
26651
< 0.1%
26621
< 0.1%
26432
< 0.1%
26412
< 0.1%

pt08_s5_o3
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

stuendlich gemitteltes Sensoransprechverhalten (nominell O3-bezogen) (Indiumoxid)

Distinct1681
Distinct (%)21.8%
Missing289
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean1024.200026
Minimum253
Maximum2523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:47.664994image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum253
5-th percentile469
Q1737
median962
Q31272
95-th percentile1756.6
Maximum2523
Range2270
Interquartile range (IQR)535

Descriptive statistics

Standard deviation393.9370249
Coefficient of variation (CV)0.3846289932
Kurtosis0.1217666533
Mean1024.200026
Median Absolute Deviation (MAD)255
Skewness0.6543408376
Sum7895558
Variance155186.3795
MonotonicityNot monotonic
2022-05-02T22:41:47.798229image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
83619
 
0.2%
82518
 
0.2%
79917
 
0.2%
82617
 
0.2%
77717
 
0.2%
73715
 
0.2%
92615
 
0.2%
92314
 
0.2%
77914
 
0.2%
85314
 
0.2%
Other values (1671)7549
94.4%
(Missing)289
 
3.6%
ValueCountFrequency (%)
2531
 
< 0.1%
2611
 
< 0.1%
2631
 
< 0.1%
2661
 
< 0.1%
2681
 
< 0.1%
2743
< 0.1%
2821
 
< 0.1%
2831
 
< 0.1%
2861
 
< 0.1%
2881
 
< 0.1%
ValueCountFrequency (%)
25231
< 0.1%
25221
< 0.1%
25191
< 0.1%
25151
< 0.1%
24801
< 0.1%
24751
< 0.1%
24651
< 0.1%
24521
< 0.1%
24341
< 0.1%
24151
< 0.1%

t
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Temperatur

Distinct419
Distinct (%)5.4%
Missing289
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean19.47604099
Minimum0.3
Maximum44.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:47.934810image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile5.7
Q113.1
median19.3
Q325.4
95-th percentile35.2
Maximum44.6
Range44.3
Interquartile range (IQR)12.3

Descriptive statistics

Standard deviation8.662273066
Coefficient of variation (CV)0.4447656005
Kurtosis-0.4715649965
Mean19.47604099
Median Absolute Deviation (MAD)6.1
Skewness0.2476629533
Sum150140.8
Variance75.03497467
MonotonicityNot monotonic
2022-05-02T22:41:48.064029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20.855
 
0.7%
21.350
 
0.6%
20.247
 
0.6%
19.846
 
0.6%
23.743
 
0.5%
21.742
 
0.5%
13.842
 
0.5%
15.642
 
0.5%
14.641
 
0.5%
19.741
 
0.5%
Other values (409)7260
90.8%
(Missing)289
 
3.6%
ValueCountFrequency (%)
0.31
 
< 0.1%
0.61
 
< 0.1%
0.83
< 0.1%
13
< 0.1%
1.23
< 0.1%
1.34
0.1%
1.44
0.1%
1.52
< 0.1%
1.62
< 0.1%
1.71
 
< 0.1%
ValueCountFrequency (%)
44.61
 
< 0.1%
44.31
 
< 0.1%
43.41
 
< 0.1%
43.11
 
< 0.1%
42.83
< 0.1%
42.71
 
< 0.1%
42.61
 
< 0.1%
42.51
 
< 0.1%
42.22
< 0.1%
422
< 0.1%

rh
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Relative Luftfeuchtigkeit

Distinct748
Distinct (%)9.7%
Missing289
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean48.96924374
Minimum9.2
Maximum88.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:48.195785image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum9.2
5-th percentile20.1
Q135.5
median49.4
Q362.1
95-th percentile77.3
Maximum88.7
Range79.5
Interquartile range (IQR)26.6

Descriptive statistics

Standard deviation17.28637789
Coefficient of variation (CV)0.3530047959
Kurtosis-0.8243365121
Mean48.96924374
Median Absolute Deviation (MAD)13.2
Skewness-0.0500365358
Sum377503.9
Variance298.8188606
MonotonicityNot monotonic
2022-05-02T22:41:48.329991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
53.129
 
0.4%
57.926
 
0.3%
50.825
 
0.3%
61.125
 
0.3%
60.825
 
0.3%
57.624
 
0.3%
50.924
 
0.3%
50.124
 
0.3%
42.823
 
0.3%
45.923
 
0.3%
Other values (738)7461
93.3%
(Missing)289
 
3.6%
ValueCountFrequency (%)
9.22
< 0.1%
9.31
< 0.1%
9.61
< 0.1%
9.81
< 0.1%
9.91
< 0.1%
102
< 0.1%
10.21
< 0.1%
10.71
< 0.1%
10.91
< 0.1%
11.61
< 0.1%
ValueCountFrequency (%)
88.71
 
< 0.1%
87.21
 
< 0.1%
87.11
 
< 0.1%
871
 
< 0.1%
86.61
 
< 0.1%
86.52
< 0.1%
861
 
< 0.1%
85.73
< 0.1%
85.61
 
< 0.1%
85.51
 
< 0.1%

ah
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
MISSING

Absolute Luftfeuchtigkeit

Distinct5916
Distinct (%)76.8%
Missing290
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean1.08221322
Minimum0.2029
Maximum2.231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size62.6 KiB
2022-05-02T22:41:48.468220image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.2029
5-th percentile0.431
Q10.81685
median1.04685
Q31.371325
95-th percentile1.74483
Maximum2.231
Range2.0281
Interquartile range (IQR)0.554475

Descriptive statistics

Standard deviation0.3945472683
Coefficient of variation (CV)0.3645744304
Kurtosis-0.5447850012
Mean1.08221322
Median Absolute Deviation (MAD)0.27505
Skewness0.1646344979
Sum8341.6995
Variance0.1556675469
MonotonicityNot monotonic
2022-05-02T22:41:48.604429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.83946
 
0.1%
1.11996
 
0.1%
0.87365
 
0.1%
0.92715
 
0.1%
0.83255
 
0.1%
0.96845
 
0.1%
1.05945
 
0.1%
0.89444
 
0.1%
1.05514
 
0.1%
1.16654
 
0.1%
Other values (5906)7659
95.8%
(Missing)290
 
3.6%
ValueCountFrequency (%)
0.20291
< 0.1%
0.2181
< 0.1%
0.21851
< 0.1%
0.21931
< 0.1%
0.23971
< 0.1%
0.2421
< 0.1%
0.24621
< 0.1%
0.24771
< 0.1%
0.25811
< 0.1%
0.25861
< 0.1%
ValueCountFrequency (%)
2.2311
< 0.1%
2.18061
< 0.1%
2.17661
< 0.1%
2.17191
< 0.1%
2.13951
< 0.1%
2.13621
< 0.1%
2.12471
< 0.1%
2.11951
< 0.1%
2.1171
< 0.1%
2.11641
< 0.1%

Interactions

2022-05-02T22:41:41.844472image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:22.673882image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:24.157966image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:25.757949image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:27.355612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:28.912533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:30.462574image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:32.042092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:33.517978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:35.152605image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:36.648060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:38.543279image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:40.012264image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:41.962970image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:22.821776image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:24.269016image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:25.891379image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:27.466275image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:29.029844image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:30.578008image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:32.158020image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:33.624727image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:35.255662image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:36.758680image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:38.653663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:40.143587image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:42.091618image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:22.933439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:24.389417image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:26.013747image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:27.581192image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:29.156925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:30.691018image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:32.273334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:33.744484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:35.375869image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:36.880323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:38.772085image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:40.293654image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:42.216892image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:23.065816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:24.512449image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:26.132306image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:27.703809image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:29.281083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:30.809517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:32.402701image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:33.867851image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:35.516875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:37.000270image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:38.894086image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:40.437029image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:42.337269image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:23.174511image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:24.626597image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:26.258534image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:27.820068image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:29.396883image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:30.926796image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:32.511996image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:33.981531image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:35.629410image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:37.120666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:39.006302image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:40.577297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:42.465141image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:23.291266image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:24.744387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:26.381424image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:27.937213image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:29.517073image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:31.041986image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:32.621388image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:34.107392image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:35.741394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2022-05-02T22:41:37.312816image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
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2022-05-02T22:41:41.717660image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2022-05-02T22:41:48.741242image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-05-02T22:41:48.933371image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-05-02T22:41:49.215976image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-05-02T22:41:49.404530image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-05-02T22:41:43.546463image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-05-02T22:41:43.834570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-05-02T22:41:44.312420image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-05-02T22:41:44.617614image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

dateco_gtpt08_s1_conmhc_gtc6h6_gtpt08_s2_nmhcnox_gtpt08_s3_noxno2_gtpt08_s4_no2pt08_s5_o3trhah
02004-03-10 18:00:002.61360.0150.011.91046.0166.01056.0113.01692.01268.013.648.90.7578
12004-03-10 19:00:002.01292.0112.09.4955.0103.01174.092.01559.0972.013.347.70.7255
22004-03-10 20:00:002.21402.088.09.0939.0131.01140.0114.01555.01074.011.954.00.7502
32004-03-10 21:00:002.21376.080.09.2948.0172.01092.0122.01584.01203.011.060.00.7867
42004-03-10 22:00:001.61272.051.06.5836.0131.01205.0116.01490.01110.011.259.60.7888
52004-03-10 23:00:001.21197.038.04.7750.089.01337.096.01393.0949.011.259.20.7848
62004-03-11 00:00:001.21185.031.03.6690.062.01462.077.01333.0733.011.356.80.7603
72004-03-11 01:00:001.01136.031.03.3672.062.01453.076.01333.0730.010.760.00.7702
82004-03-11 02:00:000.91094.024.02.3609.045.01579.060.01276.0620.010.759.70.7648
92004-03-11 03:00:000.61010.019.01.7561.0NaN1705.0NaN1235.0501.010.360.20.7517

Last rows

dateco_gtpt08_s1_conmhc_gtc6h6_gtpt08_s2_nmhcnox_gtpt08_s3_noxno2_gtpt08_s4_no2pt08_s5_o3trhah
79882005-02-06 14:00:001.0868.0NaN2.1590.0127.01081.0100.0753.0420.010.626.00.3320
79892005-02-06 15:00:000.8868.0NaN1.9576.096.01128.078.0755.0363.010.327.70.3481
79902005-02-06 16:00:001.0904.0NaN2.7633.0138.01040.0100.0789.0410.010.228.30.3516
79912005-02-06 17:00:001.4944.0NaN3.7693.0217.0928.0150.0832.0568.09.229.90.3479
79922005-02-06 18:00:001.1925.0NaN2.9649.0186.01003.0142.0819.0570.06.936.40.3635
79932005-02-06 19:00:001.6985.0NaN4.5736.0227.0891.0165.0875.0774.06.038.00.3584
79942005-02-06 20:00:001.81002.0NaN5.3780.0252.0855.0179.0892.0857.05.836.40.3385
79952005-02-06 21:00:001.4938.0NaN3.7692.0193.0937.0149.0805.0737.05.835.40.3286
79962005-02-06 22:00:001.1896.0NaN2.6627.0158.01033.0126.0782.0610.05.436.60.3304
79972005-02-06 23:00:001.0907.0NaN2.4614.0150.01052.0120.0782.0627.05.137.90.3358